ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2
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Abstract
Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850 · see citing works
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works
Highly accurate protein structure prediction for the human proteome
Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works
Varadi, M., Bertoni, D., Magana, P., Paramval, U., Pidruchna, I., Radhakrishnan, M., Tsenkov, M., Nair, S., Mirdita, M., Yeo, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad1011 · Cited by 2,052 · cited 1× · see citing works
“We classified domains from 48 whole proteomes from AFDB [32] using the DPAM v2 (Domain Parser for AlphaFold Models) domain assigner and ECOD develop285 as a reference [28,30].”
MethodECOD: an evolutionary classification of protein domains
Cheng, H., Schaeffer, R. D., Liao, Y., Kinch, L. N., Pei, J., Shi, S., Kim, B.-H., & Grishin, N. V. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003926 · Cited by 426 · see citing works
The structural coverage of the human proteome before and after AlphaFold
Porta-Pardo, E., Ruiz-Serra, V., Valentini, S., & Valencia, A. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009818 · Cited by 131 · see citing works
AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms
Bordin, N., Sillitoe, I., Nallapareddy, V., Rauer, C., Lam, S. D., Waman, V. P., Sen, N., Heinzinger, M., Littmann, M., Kim, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04488-9 · Cited by 103 · see citing works
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ECOD: Classification of domains in AFDB Swiss-Prot structure predictions
Schaeffer, R. D., Zhang, J., Cong, Q., & Grishin, N. V. (2026) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013431 · Cited by 2 · see citing works
“The AFDB is a valuable tool in structural biology, providing millions of predicted structures that ECOD has systematically classified for human proteins [21], small pathogens [22,23], and reference proteomes [24].”
Self-citation
- Authors
- Schaeffer, R. D., Zhang, J., Medvedev, K. E., Kinch, L. N., Cong, Q., & Grishin, N. V.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Schaeffer, R. Dustin
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2024-02-28
- Volume
- 20
- Issue
- 2
- Pages
- e1011586
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC10927120
- OpenAlex
- W4392230569
- Cited by
- 22
- References
- 10
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 8
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Update notices
- new_version (2024-03-11)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences (GM147367, GM127390)
- Cancer Prevention and Research Institute of Texas (RP210041)
- Division of Biological Infrastructure (2224128)
- Welch Foundation (I-2095-20220331, I-1505)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- ECOD, protein domain classification, AlphaFold, protein structure prediction, domain annotation, eukaryotes, bacteria, intrinsically disordered regions, protein repeats, TIM barrel, P-loop
- MeSH terms
- Protein Domains, Bacteria, Biological Evolution, Evolution, Molecular, Proteome, Databases, Protein
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